DBMS > BoltDB vs. ClickHouse vs. DataFS vs. HarperDB

System Properties Comparison BoltDB vs. ClickHouse vs. DataFS vs. HarperDB

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Editorial information provided by DB-Engines

Name

Description

A high-performance, column-oriented SQL DBMS for online analytical processing (OLAP) that uses all available system resources to their full potential to process each analytical query as fast as possible. It is available as both an open-source software and a cloud offering.

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

HarperDB takes the "stack" out of "tech stack" by combining an ultra-fast document-style data store, in-memory cache, real-time message broker, and your application components into a single distributed technology.

Primary database model

Secondary database models

Time Series DBMS

Graph DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.70
Rank	#225	Overall
	#31	Key-value stores

Score	15.88
Rank	#37	Overall
	#22	Relational DBMS

Score	0.02
Rank	#369	Overall
	#18	Object oriented DBMS

Score	0.55
Rank	#243	Overall
	#38	Document stores

Website

github.com/boltdb/bolt

clickhouse.com

newdatabase.com

www.harperdb.io

Technical documentation

clickhouse.com/docs

dev.mobiland.com/Overview.xsp

docs.harperdb.io/docs

Developer

Clickhouse Inc.

Mobiland AG

HarperDB

Initial release

2013

2016

2018

2017

Current release

v24.6.2.17-stable, July 2024

1.1.263, October 2022

3.1, August 2021

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache 2.0

commercial

free community edition available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

ClickHouse Cloud: Get the performance you love from open source ClickHouse in a serverless offering that takes care of the details so you can spend more time getting insight out of the fastest database on earth.
DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.

Implementation language

C++

Node.js

Server operating systems

BSD
Linux
OS X
Solaris
Windows

FreeBSD
Linux
macOS

Windows

Linux
OS X

Data scheme

schema-free

yes

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

dynamic schema

Typing

predefined data types such as float or date

yes

JSON data types

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

SQL-like data manipulation statements

APIs and other access methods

gRPC
HTTP REST
JDBC
MySQL wire protocol
ODBC
PostgreSQL wire protocol
Proprietary protocol

.NET Client API
Proprietary client DLL
WinRT client

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

Supported programming languages

3rd party library
C++
Elixir

3rd party library
Go

3rd party library
Java

3rd party library
JavaScript (Node.js)

3rd party library
Kotlin

3rd party library
Nim

3rd party library
Perl

3rd party library
PHP

3rd party library
Python

3rd party library
R

3rd party library
Ruby

3rd party library
Rust
Scala

3rd party library

.Net
C
C#
C++
VB.Net

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

Server-side scripts

Stored procedures

yes

Custom Functions

since release 3.1

Triggers

no, except callback-events from server when changes happened

Partitioning methods

Methods for storing different data on different nodes

none

key based and custom

Proprietary Sharding system

A table resides as a whole on one (or more) nodes in a cluster

Replication methods

Methods for redundantly storing data on multiple nodes

none

Asynchronous and synchronous physical replication; geographically distributed replicas; support for object storages.

yes

the nodes on which a table resides can be defined

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

yes

ACID

Atomic execution of specific operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Access rights for users and roles. Column and row based policies. Quotas and resource limits. Pluggable authentication with LDAP and Kerberos. Password based, X.509 certificate, and SSH key authentication.

Windows-Profile

Access rights for users and roles

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